#look into methods for estimating pi0 conservatively
Do some simulations
opts_knit$set(progress = TRUE, verbose = TRUE, root.dir = "~/Documents/git/ash/paper/Rcode")
require(ashr)
## Loading required package: ashr
## Loading required package: truncnorm
require(qvalue)
## Loading required package: qvalue
require(fdrtool)
## Loading required package: fdrtool
require(mixfdr)
## Loading required package: mixfdr
require(locfdr)
## Loading required package: locfdr
## Loading required package: splines
require(ggplot2)
## Loading required package: ggplot2
##
## Attaching package: 'ggplot2'
##
## The following object is masked from 'package:qvalue':
##
## qplot
# bsd gives standard deviation of beta pi0 is simulated to be uniform on
# [minpi0,1]
basicsim = function(mixsd, mixpi_alt, bsd = 1, minpi0 = 0, seedval = 100, nsamp = 1000,
niter = 50) {
set.seed(seedval)
beta = list()
betahatsd = list()
betahat = list()
zscore = list()
pval = list()
betahat.ash.n = list()
betahat.ash.u = list()
betahat.ash.npm = list()
betahat.ash.true = list()
betahat.qval = list()
betahat.fdrtool = list()
betahat.locfdr = list()
betahat.mixfdr = list()
pi0 = rep(0, niter)
for (i in 1:niter) {
pi0[i] = runif(1, minpi0, 1)
mixpi = c(pi0[i], (1 - pi0[i]) * mixpi_alt)
sd = sample(mixsd, nsamp, prob = mixpi, replace = TRUE)
beta[[i]] = rnorm(nsamp, 0, sd)
betahatsd[[i]] = bsd
betahat[[i]] = beta[[i]] + rnorm(nsamp, 0, betahatsd[[i]])
zscore[[i]] = betahat[[i]]/betahatsd[[i]]
pval[[i]] = pchisq(zscore[[i]]^2, df = 1, lower.tail = F)
betahat.ash.n[[i]] = ash(betahat[[i]], betahatsd[[i]], pointmass = TRUE,
prior = "nullbiased", gridmult = 2)
betahat.ash.u[[i]] = ash(betahat[[i]], betahatsd[[i]], pointmass = TRUE,
prior = "uniform", gridmult = 2)
betahat.ash.npm[[i]] = ash(betahat[[i]], betahatsd[[i]], pointmass = FALSE,
prior = "uniform", gridmult = 2)
betahat.ash.true[[i]] = ash(betahat[[i]], betahatsd[[i]], g = normalmix(mixpi,
rep(0, length(mixpi)), mixsd))
betahat.qval[[i]] = qvalue(pval[[i]])
betahat.fdrtool[[i]] = fdrtool(pval[[i]], statistic = "pvalue", plot = FALSE)
betahat.locfdr[[i]] = locfdr(zscore[[i]], nulltype = 0, plot = 0)
betahat.mixfdr[[i]] = mixFdr(zscore[[i]], noiseSD = 1, theonull = TRUE,
plot = FALSE)
}
return(list(beta = beta, betahatsd = betahatsd, betahat = betahat, zscore = zscore,
pval = pval, betahat.ash.n = betahat.ash.n, betahat.ash.u = betahat.ash.u,
betahat.ash.npm = betahat.ash.npm, betahat.ash.true = betahat.ash.true,
betahat.qval = betahat.qval, betahat.fdrtool = betahat.fdrtool, betahat.locfdr = betahat.locfdr,
betahat.mixfdr = betahat.mixfdr, pi0 = pi0))
}
mixsd = c(0, 0.25, 0.5, 1, 2)
mixpi_alt = c(0.4, 0.2, 0.2, 0.2) #mixture proportions under the alternative
simres1 = basicsim(mixsd, mixpi_alt, niter = 200, nsamp = 1000)
## Loading required package: fdrtool
## Loading required package: mixfdr
## Loading required package: locfdr
## Loading required package: splines
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9415 0.0215 0.0371
##
## mu = 0.000 -2.903 2.983
##
## sigma = 1.000 1.348 1.093
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9538 0.0200 0.0262
##
## mu = 0.000 3.065 -3.279
##
## sigma = 1.000 1.507 1.199
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9557 0.0325 0.0117
##
## mu = 0.000 -2.394 3.255
##
## sigma = 1.000 1.000 1.481
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9611 0.0175 0.0215
##
## mu = 0.000 -3.053 3.404
##
## sigma = 1.000 1.255 1.720
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8991 0.0529 0.0481
##
## mu = 0.000 2.724 -2.924
##
## sigma = 1.000 1.124 1.091
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9790 0.0113 0.0097
##
## mu = 0.000 2.630 -2.916
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9555 0.0222 0.0223
##
## mu = 0.000 3.141 -2.879
##
## sigma = 1.000 1.000 1.085
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9577 0.0203 0.0220
##
## mu = 0.000 -3.017 3.480
##
## sigma = 1.000 1.590 1.337
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9839 0.0085 0.0076
##
## mu = 0.000 2.900 -3.417
##
## sigma = 1.000 1.000 1.001
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9724 0.0155 0.0121
##
## mu = 0.000 -3.059 2.982
##
## sigma = 1.000 1.159 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9388 0.0323 0.0289
##
## mu = 0.000 -2.754 3.300
##
## sigma = 1.000 1.193 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9260 0.0349 0.0391
##
## mu = 0.000 -3.115 2.802
##
## sigma = 1.000 1.182 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9532 0.0130 0.0338
##
## mu = 0.000 -3.668 3.145
##
## sigma = 1.000 1.597 1.302
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9714 0.0109 0.0177
##
## mu = 0.000 -2.721 2.741
##
## sigma = 1.000 1.503 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9969 0.0019 0.0012
##
## mu = 0.000 2.942 -1.582
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9782 0.0071 0.0147
##
## mu = 0.000 3.043 -2.927
##
## sigma = 1.000 1.063 1.026
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9939 0.0051 0.0010
##
## mu = 0.000 -2.661 4.777
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9454 0.0263 0.0283
##
## mu = 0.000 -3.120 2.732
##
## sigma = 1.000 1.141 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9702 0.0142 0.0155
##
## mu = 0.000 2.934 -3.007
##
## sigma = 1.000 1.722 1.275
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9529 0.0269 0.0202
##
## mu = 0.000 2.978 -3.445
##
## sigma = 1.000 1.287 1.123
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9195 0.0441 0.0364
##
## mu = 0.000 2.816 -3.097
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9351 0.0417 0.0232
##
## mu = 0.000 2.636 -3.357
##
## sigma = 1.000 1.000 1.355
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9622 0.0192 0.0186
##
## mu = 0.000 -3.162 2.876
##
## sigma = 1.000 1.246 1.081
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9397 0.0411 0.0192
##
## mu = 0.000 2.769 -2.945
##
## sigma = 1.000 1.002 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9732 0.0134 0.0134
##
## mu = 0.000 3.312 -2.945
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9708 0.0204 0.0088
##
## mu = 0.000 -2.822 3.111
##
## sigma = 1.000 1.255 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9711 0.0200 0.0090
##
## mu = 0.000 3.288 -3.149
##
## sigma = 1.000 1.512 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9811 0.0061 0.0128
##
## mu = 0.000 3.024 -3.304
##
## sigma = 1.000 1.000 1.194
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9848 0.0074 0.0078
##
## mu = 0.000 -3.092 2.293
##
## sigma = 1.000 1.196 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9807 0.0095 0.0098
##
## mu = 0.000 -3.299 2.603
##
## sigma = 1.000 1.143 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9479 0.0248 0.0274
##
## mu = 0.000 -2.994 3.158
##
## sigma = 1.000 1.272 1.593
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9808 0.0104 0.0088
##
## mu = 0.000 -2.661 3.815
##
## sigma = 1.000 1.064 1.483
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9832 0.0121 0.0047
##
## mu = 0.000 3.765 -4.055
##
## sigma = 1.000 1.000 1.478
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9792 0.0082 0.0126
##
## mu = 0.000 -3.032 3.372
##
## sigma = 1.000 1.000 1.154
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9708 0.0195 0.0097
##
## mu = 0.000 3.059 -3.392
##
## sigma = 1.000 1.507 1.202
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Using an empirical null with a fitted noiseSD gives a
## substantially different model. Consider rerunning with theonull = FALSE
## and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9211 0.0440 0.0350
##
## mu = 0.000 2.768 -3.047
##
## sigma = 1.000 1.355 1.384
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9380 0.0349 0.0271
##
## mu = 0.000 2.990 -2.905
##
## sigma = 1.000 1.115 1.318
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9889 0.0044 0.0068
##
## mu = 0.000 -3.240 3.304
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9545 0.0235 0.0219
##
## mu = 0.000 -2.683 3.458
##
## sigma = 1.000 1.000 1.446
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9917 0.0016 0.0067
##
## mu = 0.000 -1.560 2.802
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9651 0.0117 0.0232
##
## mu = 0.000 3.371 -3.207
##
## sigma = 1.000 1.117 1.455
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9861 0.0086 0.0052
##
## mu = 0.000 -2.806 2.687
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9750 0.0176 0.0074
##
## mu = 0.000 2.489 -3.579
##
## sigma = 1.000 1.000 1.599
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9603 0.0206 0.0191
##
## mu = 0.000 2.865 -3.114
##
## sigma = 1.000 1.000 1.114
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9500 0.0248 0.0252
##
## mu = 0.000 2.016 -2.630
##
## sigma = 1.000 2.283 1.161
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9579 0.0211 0.0209
##
## mu = 0.000 3.137 -3.176
##
## sigma = 1.000 1.317 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9406 0.0243 0.0351
##
## mu = 0.000 2.983 -2.846
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9755 0.0163 0.0081
##
## mu = 0.000 2.673 -3.246
##
## sigma = 1.000 1.000 1.147
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.944 0.034 0.022
##
## mu = 0.000 -2.592 3.549
##
## sigma = 1.000 1.247 1.474
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9262 0.0354 0.0384
##
## mu = 0.000 2.868 -2.857
##
## sigma = 1.000 1.478 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9959 0.0011 0.0030
##
## mu = 0.000 0.978 -3.837
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9810 0.0142 0.0048
##
## mu = 0.000 -2.397 4.423
##
## sigma = 1.000 1.000 2.399
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE TRUE FALSE
##
## pi = 0.9983 0.0010 0.0007
##
## mu = 0.0000 -0.0783 -3.3539
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9681 0.0152 0.0168
##
## mu = 0.000 -3.057 3.402
##
## sigma = 1.000 1.247 1.237
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Using an empirical null with a fitted noiseSD gives a
## substantially different model. Consider rerunning with theonull = FALSE
## and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9179 0.0353 0.0469
##
## mu = 0.000 -2.840 2.645
##
## sigma = 1.000 1.037 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9498 0.0196 0.0306
##
## mu = 0.000 3.059 -2.780
##
## sigma = 1.000 1.019 1.283
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9387 0.0271 0.0342
##
## mu = 0.000 2.991 -3.145
##
## sigma = 1.000 1.025 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9966 0.0015 0.0019
##
## mu = 0.000 -2.864 3.398
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9725 0.0151 0.0124
##
## mu = 0.000 -2.487 3.172
##
## sigma = 1.000 1.000 1.025
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9575 0.0214 0.0210
##
## mu = 0.000 2.895 -3.148
##
## sigma = 1.000 1.487 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9713 0.0133 0.0154
##
## mu = 0.000 2.787 -3.038
##
## sigma = 1.000 1.524 1.455
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9637 0.0210 0.0153
##
## mu = 0.000 2.752 -3.361
##
## sigma = 1.000 1.000 1.237
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9384 0.0260 0.0356
##
## mu = 0.000 -2.885 2.748
##
## sigma = 1.000 1.303 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9266 0.0347 0.0387
##
## mu = 0.000 -2.714 2.864
##
## sigma = 1.000 1.315 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9948 0.0021 0.0032
##
## mu = 0.000 4.457 -2.420
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9809 0.0095 0.0096
##
## mu = 0.000 -3.555 3.221
##
## sigma = 1.000 1.000 1.353
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.961 0.015 0.024
##
## mu = 0.000 3.010 -3.143
##
## sigma = 1.000 1.197 1.056
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9271 0.0284 0.0444
##
## mu = 0.000 -2.887 2.463
##
## sigma = 1.000 1.266 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Using an empirical null with a fitted noiseSD gives a
## substantially different model. Consider rerunning with theonull = FALSE
## and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9051 0.0454 0.0495
##
## mu = 0.000 2.585 -3.210
##
## sigma = 1.000 1.145 1.158
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9109 0.0362 0.0529
##
## mu = 0.000 3.284 -2.989
##
## sigma = 1.000 1.006 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9556 0.0218 0.0227
##
## mu = 0.000 -2.905 2.877
##
## sigma = 1.000 1.295 1.227
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9766 0.0045 0.0189
##
## mu = 0.000 -3.541 2.883
##
## sigma = 1.000 1.322 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9880 0.0036 0.0085
##
## mu = 0.000 2.887 3.189
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9892 0.0036 0.0072
##
## mu = 0.000 -3.404 3.469
##
## sigma = 1.000 1.415 1.859
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9382 0.0331 0.0287
##
## mu = 0.000 -2.973 2.946
##
## sigma = 1.000 1.393 1.442
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9967 0.0010 0.0023
##
## mu = 0.000 6.207 -2.562
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9154 0.0579 0.0267
##
## mu = 0.000 3.170 -3.164
##
## sigma = 1.000 1.000 1.017
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9307 0.0392 0.0301
##
## mu = 0.000 2.790 -2.554
##
## sigma = 1.000 1.125 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9969 0.0018 0.0014
##
## mu = 0.000 2.143 -1.274
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9911 0.0056 0.0032
##
## mu = 0.000 2.207 2.117
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9866 0.0060 0.0074
##
## mu = 0.00 -2.90 3.41
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9821 0.0142 0.0037
##
## mu = 0.000 2.845 -4.000
##
## sigma = 1.00 1.00 1.18
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9877 0.0068 0.0055
##
## mu = 0.000 2.721 -2.824
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9945 0.0030 0.0025
##
## mu = 0.0000 2.2668 0.8778
##
## sigma = 1.000 3.572 3.453
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9750 0.0094 0.0156
##
## mu = 0.000 -3.652 3.160
##
## sigma = 1.000 1.411 1.377
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9942 0.0040 0.0018
##
## mu = 0.0000 1.2871 0.4115
##
## sigma = 1.000 4.283 1.385
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9823 0.0059 0.0118
##
## mu = 0.000 -3.059 3.261
##
## sigma = 1.000 1.000 1.232
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9916 0.0033 0.0051
##
## mu = 0.000 3.370 -3.434
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9455 0.0350 0.0195
##
## mu = 0.000 2.720 -2.691
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9755 0.0170 0.0074
##
## mu = 0.000 3.595 -3.456
##
## sigma = 1.000 1.204 1.261
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9823 0.0104 0.0072
##
## mu = 0.000 2.939 -3.024
##
## sigma = 1.000 1.000 1.127
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9748 0.0117 0.0135
##
## mu = 0.000 -3.334 2.721
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9827 0.0070 0.0103
##
## mu = 0.000 3.577 -3.805
##
## sigma = 1.000 2.408 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9624 0.0182 0.0194
##
## mu = 0.000 -2.681 3.093
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9945 0.0020 0.0035
##
## mu = 0.000 -5.404 2.826
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9459 0.0202 0.0339
##
## mu = 0.000 3.163 -2.994
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9618 0.0130 0.0251
##
## mu = 0.000 -2.778 3.407
##
## sigma = 1.000 1.088 1.253
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9745 0.0129 0.0125
##
## mu = 0.000 -2.922 3.375
##
## sigma = 1.000 1.684 1.422
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9778 0.0103 0.0120
##
## mu = 0.000 -3.447 3.061
##
## sigma = 1.000 2.021 1.425
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9603 0.0212 0.0185
##
## mu = 0.000 2.476 -2.891
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9391 0.0272 0.0337
##
## mu = 0.000 -3.484 2.990
##
## sigma = 1.000 1.641 1.362
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9699 0.0094 0.0207
##
## mu = 0.000 3.530 -3.279
##
## sigma = 1.000 1.169 1.128
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9798 0.0100 0.0102
##
## mu = 0.000 2.980 -3.135
##
## sigma = 1.000 1.000 1.017
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9215 0.0382 0.0404
##
## mu = 0.000 3.051 -2.865
##
## sigma = 1.000 1.109 1.071
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9718 0.0062 0.0220
##
## mu = 0.000 -3.166 2.811
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE TRUE FALSE
##
## pi = 0.9929 0.0010 0.0061
##
## mu = 0.0000 0.1216 3.3177
##
## sigma = 1.000 1.000 1.097
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9778 0.0074 0.0148
##
## mu = 0.000 -2.514 3.325
##
## sigma = 1.00 1.00 1.31
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9728 0.0204 0.0068
##
## mu = 0.000 -2.811 3.151
##
## sigma = 1.000 1.264 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9412 0.0233 0.0354
##
## mu = 0.000 -3.014 3.249
##
## sigma = 1.000 1.115 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9837 0.0030 0.0133
##
## mu = 0.000 2.753 2.650
##
## sigma = 1.000 1.107 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9678 0.0139 0.0182
##
## mu = 0.000 2.995 -2.969
##
## sigma = 1.000 1.286 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9626 0.0132 0.0242
##
## mu = 0.000 -2.996 2.837
##
## sigma = 1.000 1.423 1.021
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9451 0.0229 0.0320
##
## mu = 0.000 -3.187 2.954
##
## sigma = 1.000 1.181 1.437
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9834 0.0061 0.0106
##
## mu = 0.000 2.996 -2.736
##
## sigma = 1.000 2.003 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9425 0.0213 0.0362
##
## mu = 0.000 3.481 -2.939
##
## sigma = 1.000 1.226 1.105
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9912 0.0076 0.0011
##
## mu = 0.000 -2.996 1.014
##
## sigma = 1.000 1.199 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9919 0.0020 0.0061
##
## mu = 0.000 -5.185 3.683
##
## sigma = 1.000 1.000 1.097
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9778 0.0109 0.0113
##
## mu = 0.000 -3.268 2.557
##
## sigma = 1.000 1.214 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9132 0.0517 0.0351
##
## mu = 0.000 2.871 -2.804
##
## sigma = 1.000 1.239 1.473
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9393 0.0356 0.0252
##
## mu = 0.000 -2.878 3.171
##
## sigma = 1.000 1.259 1.208
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9376 0.0438 0.0186
##
## mu = 0.000 -2.801 3.197
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9622 0.0178 0.0199
##
## mu = 0.000 -2.861 3.339
##
## sigma = 1.000 1.000 1.534
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9549 0.0218 0.0232
##
## mu = 0.000 -2.868 2.775
##
## sigma = 1.000 1.000 1.039
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9642 0.0162 0.0197
##
## mu = 0.000 -2.834 2.863
##
## sigma = 1.000 1.116 1.090
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9464 0.0310 0.0226
##
## mu = 0.000 2.890 -2.669
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9874 0.0091 0.0034
##
## mu = 0.000 3.473 -3.207
##
## sigma = 1.000 1.000 1.125
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9892 0.0064 0.0044
##
## mu = 0.000 -3.773 3.982
##
## sigma = 1.000 1.000 1.831
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9332 0.0318 0.0350
##
## mu = 0.000 -3.045 2.888
##
## sigma = 1.000 1.143 1.003
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Using an empirical null with a fitted noiseSD gives a
## substantially different model. Consider rerunning with theonull = FALSE
## and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9023 0.0500 0.0477
##
## mu = 0.000 3.108 -2.790
##
## sigma = 1.000 1.373 1.567
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9471 0.0274 0.0255
##
## mu = 0.000 -3.434 2.729
##
## sigma = 1.000 1.001 1.053
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9950 0.0019 0.0031
##
## mu = 0.000 -2.499 -2.646
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9951 0.0028 0.0021
##
## mu = 0.000 -4.137 3.900
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9645 0.0141 0.0214
##
## mu = 0.000 -3.064 2.670
##
## sigma = 1.000 1.529 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9502 0.0211 0.0287
##
## mu = 0.000 -3.202 2.971
##
## sigma = 1.000 1.306 1.015
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9710 0.0165 0.0125
##
## mu = 0.000 -3.113 3.008
##
## sigma = 1.000 1.422 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9709 0.0106 0.0184
##
## mu = 0.000 -3.154 2.755
##
## sigma = 1.000 1.106 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9444 0.0311 0.0245
##
## mu = 0.000 3.582 -2.892
##
## sigma = 1.000 1.194 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9457 0.0325 0.0218
##
## mu = 0.000 -3.063 2.998
##
## sigma = 1.000 1.023 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9675 0.0174 0.0150
##
## mu = 0.000 -2.744 3.837
##
## sigma = 1.000 1.091 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9927 0.0024 0.0050
##
## mu = 0.000 2.292 -2.804
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9877 0.0044 0.0079
##
## mu = 0.000 2.904 -3.694
##
## sigma = 1.00 1.04 1.00
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9174 0.0395 0.0431
##
## mu = 0.000 2.645 -2.642
##
## sigma = 1.000 1.000 1.152
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9718 0.0135 0.0148
##
## mu = 0.000 -3.483 2.892
##
## sigma = 1.000 1.153 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9511 0.0250 0.0240
##
## mu = 0.000 2.778 -2.802
##
## sigma = 1.000 1.000 1.201
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9891 0.0019 0.0090
##
## mu = 0.000 -5.083 3.563
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9278 0.0282 0.0440
##
## mu = 0.000 -2.867 2.964
##
## sigma = 1.000 1.115 1.294
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9834 0.0077 0.0089
##
## mu = 0.000 2.731 -3.335
##
## sigma = 1.000 1.000 1.272
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9354 0.0230 0.0417
##
## mu = 0.000 -3.015 2.530
##
## sigma = 1.000 1.456 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9775 0.0138 0.0087
##
## mu = 0.000 3.394 -3.526
##
## sigma = 1.000 1.034 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9560 0.0251 0.0190
##
## mu = 0.000 2.973 -2.960
##
## sigma = 1.000 1.227 1.084
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9700 0.0138 0.0162
##
## mu = 0.000 -2.652 2.787
##
## sigma = 1.000 1.255 1.397
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9877 0.0042 0.0081
##
## mu = 0.000 -4.178 3.056
##
## sigma = 1.000 1.000 1.388
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9153 0.0528 0.0319
##
## mu = 0.000 -2.963 3.072
##
## sigma = 1.000 1.215 1.125
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9286 0.0309 0.0406
##
## mu = 0.000 -2.678 2.735
##
## sigma = 1.000 1.000 1.554
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9703 0.0142 0.0156
##
## mu = 0.000 2.982 -2.956
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9892 0.0076 0.0032
##
## mu = 0.000 3.047 -2.686
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9709 0.0161 0.0130
##
## mu = 0.000 -2.529 2.488
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9383 0.0309 0.0308
##
## mu = 0.000 3.174 -3.154
##
## sigma = 1.000 1.353 1.167
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9454 0.0309 0.0236
##
## mu = 0.000 -2.780 3.206
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9825 0.0113 0.0062
##
## mu = 0.000 -3.812 3.558
##
## sigma = 1.000 1.363 1.644
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9309 0.0348 0.0344
##
## mu = 0.000 2.827 -3.098
##
## sigma = 1.000 1.071 1.127
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9929 0.0027 0.0044
##
## mu = 0.000 -3.328 3.042
##
## sigma = 1.000 1.612 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9964 0.0017 0.0019
##
## mu = 0.000 -4.213 3.195
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9456 0.0214 0.0330
##
## mu = 0.000 -2.996 2.873
##
## sigma = 1.000 1.000 1.137
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9902 0.0051 0.0047
##
## mu = 0.000 2.524 -2.516
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9887 0.0023 0.0090
##
## mu = 0.000 -4.542 2.408
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9573 0.0171 0.0256
##
## mu = 0.000 2.971 -2.924
##
## sigma = 1.000 1.000 1.266
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9910 0.0071 0.0019
##
## mu = 0.000 -2.567 2.257
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9417 0.0307 0.0276
##
## mu = 0.000 -2.914 3.100
##
## sigma = 1.00 1.05 1.00
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9936 0.0029 0.0036
##
## mu = 0.000 4.636 -4.455
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9936 0.0039 0.0024
##
## mu = 0.000 2.695 -3.872
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9187 0.0440 0.0373
##
## mu = 0.000 2.790 -2.988
##
## sigma = 1.000 1.171 1.166
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9628 0.0131 0.0241
##
## mu = 0.000 -3.212 3.211
##
## sigma = 1.000 1.765 1.667
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9910 0.0034 0.0056
##
## mu = 0.000 3.533 -4.147
##
## sigma = 1.000 1.578 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9652 0.0133 0.0215
##
## mu = 0.000 3.083 -3.081
##
## sigma = 1.000 1.293 1.527
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9974 0.0016 0.0011
##
## mu = 0.0000 -3.2379 0.3495
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9684 0.0165 0.0151
##
## mu = 0.000 3.081 -3.444
##
## sigma = 1.000 1.292 1.133
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9973 0.0012 0.0015
##
## mu = 0.000 -4.514 1.437
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9348 0.0240 0.0412
##
## mu = 0.000 -3.007 2.587
##
## sigma = 1.000 2.266 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9822 0.0069 0.0109
##
## mu = 0.000 3.529 -3.241
##
## sigma = 1.000 1.446 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9649 0.0184 0.0167
##
## mu = 0.000 3.453 -2.470
##
## sigma = 1.000 1.000 2.387
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.996 0.003 0.001
##
## mu = 0.000 -2.752 7.333
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9499 0.0251 0.0249
##
## mu = 0.000 3.151 -2.952
##
## sigma = 1.000 1.275 1.172
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9554 0.0238 0.0208
##
## mu = 0.000 3.055 -3.009
##
## sigma = 1.000 1.553 1.250
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9529 0.0221 0.0250
##
## mu = 0.000 3.005 -2.517
##
## sigma = 1.000 1.278 1.509
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9728 0.0111 0.0161
##
## mu = 0.000 3.247 -2.860
##
## sigma = 1.000 1.103 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9717 0.0119 0.0164
##
## mu = 0.000 -2.676 3.320
##
## sigma = 1.000 1.000 1.139
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9434 0.0338 0.0228
##
## mu = 0.000 -2.761 3.005
##
## sigma = 1.00 1.00 1.27
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9780 0.0128 0.0092
##
## mu = 0.000 -3.162 3.198
##
## sigma = 1.000 1.164 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9203 0.0269 0.0528
##
## mu = 0.000 3.334 -2.768
##
## sigma = 1.000 1.197 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9466 0.0244 0.0290
##
## mu = 0.000 2.922 -3.143
##
## sigma = 1.000 1.093 1.430
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9159 0.0379 0.0463
##
## mu = 0.000 -2.643 3.061
##
## sigma = 1.000 1.060 1.331
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9683 0.0125 0.0191
##
## mu = 0.000 -2.998 3.052
##
## sigma = 1.000 1.246 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9347 0.0307 0.0346
##
## mu = 0.000 -3.183 2.735
##
## sigma = 1.000 1.083 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Using an empirical null with a fitted noiseSD gives a
## substantially different model. Consider rerunning with theonull = FALSE
## and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9081 0.0445 0.0474
##
## mu = 0.000 2.848 -2.761
##
## sigma = 1.000 1.024 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9805 0.0085 0.0110
##
## mu = 0.000 -2.866 2.683
##
## sigma = 1.000 1.341 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9076 0.0549 0.0375
##
## mu = 0.000 -2.626 3.083
##
## sigma = 1.000 1.000 1.341
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9731 0.0132 0.0137
##
## mu = 0.000 -3.257 2.686
##
## sigma = 1.000 1.148 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9976 0.0013 0.0011
##
## mu = 0.000 -2.842 -1.161
##
## sigma = 1.00 1.00 1.32
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9792 0.0120 0.0088
##
## mu = 0.000 2.985 -2.922
##
## sigma = 1.000 1.258 1.000
##
## noiseSD = 1
simres1a = basicsim(mixsd, mixpi_alt, niter = 200, nsamp = 10000)
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9456 0.0253 0.0292
##
## mu = 0.000 2.987 -3.008
##
## sigma = 1.000 1.214 1.373
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9369 0.0308 0.0323
##
## mu = 0.000 2.888 -2.878
##
## sigma = 1.000 1.082 1.190
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9789 0.0105 0.0106
##
## mu = 0.000 -2.816 2.476
##
## sigma = 1.000 1.160 1.663
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9659 0.0186 0.0154
##
## mu = 0.000 2.774 -2.981
##
## sigma = 1.000 1.304 1.229
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9300 0.0341 0.0360
##
## mu = 0.000 -3.048 2.899
##
## sigma = 1.000 1.228 1.047
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9209 0.0405 0.0386
##
## mu = 0.000 2.949 -2.835
##
## sigma = 1.000 1.258 1.204
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9861 0.0073 0.0067
##
## mu = 0.000 2.128 -2.488
##
## sigma = 1.000 1.996 1.187
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9289 0.0363 0.0348
##
## mu = 0.000 2.872 -2.867
##
## sigma = 1.000 1.217 1.072
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9354 0.0309 0.0337
##
## mu = 0.000 2.937 -2.991
##
## sigma = 1.000 1.240 1.141
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9854 0.0073 0.0073
##
## mu = 0.000 -2.791 2.359
##
## sigma = 1.000 1.129 1.155
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9249 0.0400 0.0352
##
## mu = 0.000 2.689 -2.925
##
## sigma = 1.000 1.252 1.164
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9729 0.0135 0.0136
##
## mu = 0.000 -2.795 3.115
##
## sigma = 1.000 1.513 1.242
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9661 0.0185 0.0154
##
## mu = 0.000 2.886 -2.718
##
## sigma = 1.000 1.415 1.169
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9555 0.0201 0.0244
##
## mu = 0.000 2.952 -2.763
##
## sigma = 1.000 1.274 1.201
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9854 0.0074 0.0072
##
## mu = 0.000 2.664 -1.569
##
## sigma = 1.000 1.345 2.330
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9873 0.0057 0.0070
##
## mu = 0.000 -2.536 2.697
##
## sigma = 1.000 1.563 1.383
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9422 0.0254 0.0324
##
## mu = 0.000 3.036 -2.893
##
## sigma = 1.000 1.279 1.128
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9825 0.0089 0.0087
##
## mu = 0.000 -2.834 2.986
##
## sigma = 1.000 1.197 1.429
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9398 0.0318 0.0284
##
## mu = 0.000 -2.913 3.009
##
## sigma = 1.000 1.107 1.206
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9864 0.0054 0.0081
##
## mu = 0.000 -2.338 2.520
##
## sigma = 1.000 1.799 1.337
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9396 0.0287 0.0317
##
## mu = 0.000 2.865 -2.770
##
## sigma = 1.000 1.084 1.355
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9868 0.0066 0.0067
##
## mu = 0.000 -2.393 2.751
##
## sigma = 1.000 1.510 1.299
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9569 0.0223 0.0209
##
## mu = 0.000 3.010 -2.979
##
## sigma = 1.000 1.154 1.123
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9621 0.0203 0.0176
##
## mu = 0.000 3.212 -3.092
##
## sigma = 1.000 1.249 1.268
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9510 0.0238 0.0252
##
## mu = 0.000 2.927 -2.797
##
## sigma = 1.000 1.248 1.159
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9653 0.0184 0.0163
##
## mu = 0.000 -2.986 3.160
##
## sigma = 1.000 1.297 1.244
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9341 0.0321 0.0338
##
## mu = 0.000 3.003 -2.614
##
## sigma = 1.000 1.279 1.264
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9793 0.0096 0.0111
##
## mu = 0.000 -2.858 2.930
##
## sigma = 1.000 1.198 1.456
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9621 0.0200 0.0179
##
## mu = 0.000 2.976 -2.948
##
## sigma = 1.000 1.271 1.250
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9832 0.0083 0.0085
##
## mu = 0.000 2.718 -2.696
##
## sigma = 1.000 1.378 1.154
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9610 0.0172 0.0218
##
## mu = 0.000 -2.942 2.884
##
## sigma = 1.000 1.208 1.196
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE TRUE
##
## pi = 0.9885 0.0061 0.0054
##
## mu = 0.0000 -0.4364 -0.0224
##
## sigma = 1.000 2.194 1.252
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9409 0.0300 0.0291
##
## mu = 0.000 2.946 -3.000
##
## sigma = 1.000 1.187 1.067
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.919 0.044 0.037
##
## mu = 0.000 2.775 -2.918
##
## sigma = 1.000 1.203 1.300
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9098 0.0473 0.0429
##
## mu = 0.000 2.876 -2.945
##
## sigma = 1.000 1.197 1.194
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9465 0.0271 0.0264
##
## mu = 0.000 2.844 -2.874
##
## sigma = 1.000 1.290 1.099
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9778 0.0102 0.0120
##
## mu = 0.000 3.047 -2.880
##
## sigma = 1.000 1.308 1.409
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9533 0.0236 0.0231
##
## mu = 0.000 -2.815 3.017
##
## sigma = 1.000 1.212 1.115
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9898 0.0051 0.0051
##
## mu = 0.0000 -0.5848 1.2232
##
## sigma = 1.000 1.023 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9301 0.0352 0.0347
##
## mu = 0.000 -2.876 2.874
##
## sigma = 1.000 1.262 1.182
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9373 0.0312 0.0315
##
## mu = 0.000 2.922 -2.946
##
## sigma = 1.000 1.105 1.082
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9216 0.0379 0.0405
##
## mu = 0.000 -2.866 2.937
##
## sigma = 1.000 1.049 1.178
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9592 0.0184 0.0224
##
## mu = 0.000 -3.133 2.870
##
## sigma = 1.000 1.100 1.223
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9375 0.0285 0.0340
##
## mu = 0.000 2.931 -2.731
##
## sigma = 1.000 1.108 1.290
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9845 0.0076 0.0078
##
## mu = 0.000 -2.564 2.887
##
## sigma = 1.000 1.807 1.454
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9798 0.0096 0.0105
##
## mu = 0.000 -3.206 2.676
##
## sigma = 1.000 1.148 1.300
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9887 0.0056 0.0057
##
## mu = 0.000 -2.266 2.434
##
## sigma = 1.000 1.235 1.869
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9798 0.0100 0.0101
##
## mu = 0.000 -2.737 3.000
##
## sigma = 1.00 1.30 1.31
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9655 0.0199 0.0147
##
## mu = 0.000 -3.020 2.891
##
## sigma = 1.000 1.174 1.176
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9586 0.0221 0.0193
##
## mu = 0.000 -2.752 2.966
##
## sigma = 1.000 1.441 1.263
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9218 0.0385 0.0397
##
## mu = 0.000 2.813 -2.724
##
## sigma = 1.000 1.248 1.312
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9270 0.0363 0.0366
##
## mu = 0.000 -2.954 2.895
##
## sigma = 1.000 1.211 1.230
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9687 0.0162 0.0150
##
## mu = 0.000 -3.037 2.887
##
## sigma = 1.000 1.131 1.337
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9348 0.0326 0.0326
##
## mu = 0.000 -2.941 2.914
##
## sigma = 1.000 1.114 1.207
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE TRUE FALSE
##
## pi = 0.9899 0.0051 0.0050
##
## mu = 0.0000 0.0999 0.9951
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9770 0.0108 0.0122
##
## mu = 0.000 -3.091 2.749
##
## sigma = 1.000 1.221 1.255
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9832 0.0092 0.0076
##
## mu = 0.000 2.743 -2.787
##
## sigma = 1.000 1.355 1.236
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9505 0.0255 0.0240
##
## mu = 0.000 2.942 -3.062
##
## sigma = 1.000 1.159 1.169
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9486 0.0259 0.0255
##
## mu = 0.000 3.002 -2.912
##
## sigma = 1.000 1.237 1.151
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9310 0.0362 0.0328
##
## mu = 0.000 2.848 -2.908
##
## sigma = 1.000 1.165 1.157
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9862 0.0076 0.0063
##
## mu = 0.00 -2.26 1.13
##
## sigma = 1.000 1.196 2.219
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9761 0.0099 0.0140
##
## mu = 0.000 3.246 -2.912
##
## sigma = 1.000 1.142 1.077
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9809 0.0098 0.0093
##
## mu = 0.000 2.447 -2.779
##
## sigma = 1.000 1.609 1.232
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9786 0.0103 0.0112
##
## mu = 0.000 -2.584 3.007
##
## sigma = 1.000 1.482 1.212
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9235 0.0407 0.0359
##
## mu = 0.000 -2.692 2.705
##
## sigma = 1.000 1.273 1.256
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9497 0.0236 0.0267
##
## mu = 0.000 -2.918 2.685
##
## sigma = 1.000 1.277 1.259
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9789 0.0101 0.0111
##
## mu = 0.000 2.444 -2.780
##
## sigma = 1.000 1.301 1.358
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9405 0.0289 0.0305
##
## mu = 0.000 3.078 -2.882
##
## sigma = 1.000 1.119 1.109
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9217 0.0383 0.0400
##
## mu = 0.000 2.841 -2.926
##
## sigma = 1.000 1.127 1.115
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9806 0.0094 0.0100
##
## mu = 0.000 -2.922 2.695
##
## sigma = 1.000 1.212 1.763
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE TRUE FALSE
##
## pi = 0.9894 0.0053 0.0053
##
## mu = 0.0000 -0.1583 -0.3238
##
## sigma = 1.000 1.000 2.074
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9520 0.0272 0.0208
##
## mu = 0.000 2.927 -2.965
##
## sigma = 1.000 1.138 1.171
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9880 0.0059 0.0061
##
## mu = 0.000 -2.536 2.341
##
## sigma = 1.000 2.000 1.334
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9452 0.0292 0.0255
##
## mu = 0.000 -2.793 2.876
##
## sigma = 1.000 1.232 1.157
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9616 0.0215 0.0169
##
## mu = 0.000 -2.635 2.937
##
## sigma = 1.000 1.202 1.265
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9205 0.0394 0.0401
##
## mu = 0.000 2.769 -2.742
##
## sigma = 1.000 1.146 1.180
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9685 0.0159 0.0156
##
## mu = 0.000 -2.904 3.014
##
## sigma = 1.000 1.177 1.474
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9578 0.0210 0.0212
##
## mu = 0.000 -2.796 2.716
##
## sigma = 1.000 1.347 1.250
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9550 0.0214 0.0236
##
## mu = 0.000 -2.873 2.841
##
## sigma = 1.000 1.159 1.123
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9641 0.0178 0.0181
##
## mu = 0.000 2.937 -2.778
##
## sigma = 1.000 1.323 1.389
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9495 0.0244 0.0261
##
## mu = 0.000 3.095 -2.949
##
## sigma = 1.000 1.243 1.175
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9266 0.0402 0.0333
##
## mu = 0.000 -2.906 2.977
##
## sigma = 1.000 1.269 1.179
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9128 0.0412 0.0460
##
## mu = 0.000 -2.924 2.853
##
## sigma = 1.000 1.172 1.159
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9731 0.0129 0.0140
##
## mu = 0.000 2.864 -2.807
##
## sigma = 1.000 1.315 1.331
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9375 0.0318 0.0307
##
## mu = 0.000 -2.915 2.966
##
## sigma = 1.000 1.245 1.135
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9254 0.0352 0.0394
##
## mu = 0.000 -2.795 2.814
##
## sigma = 1.000 1.201 1.232
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9309 0.0346 0.0345
##
## mu = 0.000 2.899 -2.868
##
## sigma = 1.000 1.160 1.168
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9815 0.0094 0.0091
##
## mu = 0.000 2.463 -3.006
##
## sigma = 1.000 1.496 1.306
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9170 0.0352 0.0479
##
## mu = 0.000 -2.999 2.804
##
## sigma = 1.000 1.240 1.204
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9686 0.0155 0.0159
##
## mu = 0.000 -2.901 2.990
##
## sigma = 1.000 1.606 1.170
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9381 0.0300 0.0319
##
## mu = 0.000 -3.034 2.962
##
## sigma = 1.000 1.145 1.192
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9914 0.0038 0.0048
##
## mu = 0.0000 0.5603 -0.8404
##
## sigma = 1.000 2.302 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9740 0.0131 0.0129
##
## mu = 0.000 -2.892 2.811
##
## sigma = 1.000 1.438 1.361
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9500 0.0247 0.0253
##
## mu = 0.000 2.951 -2.905
##
## sigma = 1.000 1.176 1.309
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9862 0.0063 0.0075
##
## mu = 0.000 -2.196 2.511
##
## sigma = 1.000 1.025 1.306
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9530 0.0257 0.0214
##
## mu = 0.000 -2.710 2.952
##
## sigma = 1.000 1.297 1.210
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.953 0.023 0.024
##
## mu = 0.000 -2.951 3.039
##
## sigma = 1.000 1.241 1.237
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9847 0.0086 0.0067
##
## mu = 0.000 2.930 -2.752
##
## sigma = 1.000 1.318 1.215
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9807 0.0104 0.0089
##
## mu = 0.000 2.759 -2.802
##
## sigma = 1.000 1.350 1.204
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9677 0.0153 0.0170
##
## mu = 0.000 2.920 -2.986
##
## sigma = 1.000 1.209 1.350
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9182 0.0417 0.0401
##
## mu = 0.000 2.950 -2.918
##
## sigma = 1.000 1.041 1.124
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9108 0.0414 0.0478
##
## mu = 0.000 2.918 -2.906
##
## sigma = 1.000 1.054 1.167
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9447 0.0291 0.0262
##
## mu = 0.000 -3.066 2.941
##
## sigma = 1.000 1.161 1.009
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE TRUE
##
## pi = 0.9883 0.0059 0.0058
##
## mu = 0.0000 0.5146 -0.1534
##
## sigma = 1.000 2.091 2.804
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9879 0.0058 0.0064
##
## mu = 0.000 -1.287 1.352
##
## sigma = 1.000 1.840 1.727
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9578 0.0207 0.0216
##
## mu = 0.000 2.755 -2.985
##
## sigma = 1.000 1.228 1.244
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9330 0.0354 0.0316
##
## mu = 0.000 3.019 -2.935
##
## sigma = 1.000 1.167 1.127
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9689 0.0142 0.0169
##
## mu = 0.000 -2.932 2.826
##
## sigma = 1.000 1.415 1.196
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9248 0.0386 0.0366
##
## mu = 0.000 -2.851 3.008
##
## sigma = 1.000 1.148 1.329
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9864 0.0071 0.0065
##
## mu = 0.000 -2.458 2.536
##
## sigma = 1.000 1.525 1.099
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9194 0.0394 0.0412
##
## mu = 0.000 -2.850 2.841
##
## sigma = 1.000 1.126 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9756 0.0117 0.0126
##
## mu = 0.000 -3.083 2.763
##
## sigma = 1.000 1.189 1.291
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9241 0.0388 0.0371
##
## mu = 0.000 2.878 -2.915
##
## sigma = 1.000 1.176 1.260
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9872 0.0061 0.0066
##
## mu = 0.000 2.757 -1.620
##
## sigma = 1.000 1.290 1.685
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9233 0.0402 0.0364
##
## mu = 0.000 2.945 -2.884
##
## sigma = 1.000 1.130 1.148
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9355 0.0296 0.0348
##
## mu = 0.000 -2.823 2.904
##
## sigma = 1.000 1.239 1.232
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9653 0.0164 0.0183
##
## mu = 0.000 -3.060 2.973
##
## sigma = 1.000 1.193 1.100
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9720 0.0162 0.0119
##
## mu = 0.000 2.820 -2.784
##
## sigma = 1.000 1.526 1.045
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9685 0.0149 0.0166
##
## mu = 0.000 -2.997 2.725
##
## sigma = 1.000 1.415 1.296
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9707 0.0138 0.0155
##
## mu = 0.000 2.959 -2.802
##
## sigma = 1.000 1.311 1.380
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9316 0.0341 0.0343
##
## mu = 0.000 2.912 -2.972
##
## sigma = 1.000 1.107 1.087
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9234 0.0381 0.0385
##
## mu = 0.000 2.938 -2.827
##
## sigma = 1.000 1.204 1.014
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9316 0.0336 0.0348
##
## mu = 0.000 2.763 -2.829
##
## sigma = 1.000 1.271 1.221
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9479 0.0259 0.0262
##
## mu = 0.000 3.074 -3.015
##
## sigma = 1.000 1.147 1.206
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9221 0.0384 0.0395
##
## mu = 0.000 2.847 -2.924
##
## sigma = 1.000 1.183 1.185
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9854 0.0084 0.0063
##
## mu = 0.000 2.652 -2.248
##
## sigma = 1.000 1.000 1.027
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9428 0.0276 0.0296
##
## mu = 0.000 3.029 -3.030
##
## sigma = 1.000 1.144 1.018
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9805 0.0098 0.0097
##
## mu = 0.000 2.436 -2.943
##
## sigma = 1.000 1.758 1.110
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9486 0.0257 0.0257
##
## mu = 0.000 2.845 -2.933
##
## sigma = 1.000 1.434 1.310
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.978 0.012 0.010
##
## mu = 0.000 -2.882 3.073
##
## sigma = 1.000 1.179 1.368
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9777 0.0116 0.0107
##
## mu = 0.000 -2.976 3.158
##
## sigma = 1.000 1.331 1.332
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9466 0.0249 0.0285
##
## mu = 0.000 3.216 -2.876
##
## sigma = 1.000 1.238 1.091
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9545 0.0226 0.0229
##
## mu = 0.000 -3.030 2.989
##
## sigma = 1.000 1.270 1.192
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9741 0.0116 0.0143
##
## mu = 0.000 -2.694 2.850
##
## sigma = 1.000 1.502 1.000
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9465 0.0266 0.0269
##
## mu = 0.000 -2.908 2.672
##
## sigma = 1.000 1.022 1.445
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9650 0.0162 0.0188
##
## mu = 0.000 -2.831 2.822
##
## sigma = 1.000 1.334 1.320
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9266 0.0389 0.0345
##
## mu = 0.000 2.877 -2.874
##
## sigma = 1.000 1.301 1.198
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9750 0.0112 0.0137
##
## mu = 0.000 2.960 -2.739
##
## sigma = 1.000 1.203 1.100
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9587 0.0213 0.0200
##
## mu = 0.000 2.855 -3.079
##
## sigma = 1.000 1.268 1.079
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9776 0.0104 0.0120
##
## mu = 0.000 2.983 -2.736
##
## sigma = 1.000 1.285 1.231
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9573 0.0214 0.0213
##
## mu = 0.000 2.874 -2.649
##
## sigma = 1.000 1.318 1.458
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE TRUE FALSE
##
## pi = 0.9894 0.0055 0.0051
##
## mu = 0.0000 0.1537 0.4573
##
## sigma = 1.000 2.661 1.309
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9175 0.0396 0.0428
##
## mu = 0.000 -2.990 2.828
##
## sigma = 1.000 1.142 1.198
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9865 0.0068 0.0066
##
## mu = 0.000 -2.544 2.506
##
## sigma = 1.000 1.797 1.833
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9275 0.0355 0.0370
##
## mu = 0.000 3.061 -2.864
##
## sigma = 1.000 1.128 1.355
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9858 0.0069 0.0073
##
## mu = 0.000 -2.072 2.341
##
## sigma = 1.000 1.727 1.466
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9544 0.0228 0.0228
##
## mu = 0.000 2.775 -2.977
##
## sigma = 1.000 1.228 1.190
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.985 0.007 0.008
##
## mu = 0.000 -2.252 2.815
##
## sigma = 1.000 1.202 1.119
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9875 0.0072 0.0053
##
## mu = 0.000 2.271 -2.545
##
## sigma = 1.000 1.698 1.470
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9874 0.0060 0.0066
##
## mu = 0.0000 -0.9812 1.7210
##
## sigma = 1.000 2.227 1.698
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9800 0.0095 0.0104
##
## mu = 0.000 -2.762 2.582
##
## sigma = 1.000 1.439 1.121
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9131 0.0458 0.0411
##
## mu = 0.000 2.872 -2.907
##
## sigma = 1.000 1.135 1.150
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9408 0.0305 0.0286
##
## mu = 0.000 -3.053 2.968
##
## sigma = 1.000 1.210 1.202
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9821 0.0095 0.0085
##
## mu = 0.000 -2.447 2.859
##
## sigma = 1.000 1.123 1.365
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9263 0.0343 0.0394
##
## mu = 0.000 -2.848 2.839
##
## sigma = 1.000 1.111 1.141
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9445 0.0270 0.0286
##
## mu = 0.000 2.830 -2.947
##
## sigma = 1.000 1.245 1.196
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9598 0.0214 0.0188
##
## mu = 0.000 -2.689 2.976
##
## sigma = 1.000 1.304 1.242
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9885 0.0054 0.0061
##
## mu = 0.0000 -0.4952 1.6357
##
## sigma = 1.000 2.272 1.474
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9896 0.0053 0.0051
##
## mu = 0.0000 0.3547 -1.4221
##
## sigma = 1.000 1.059 1.241
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9852 0.0068 0.0080
##
## mu = 0.000 2.926 -2.458
##
## sigma = 1.000 1.382 1.240
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9467 0.0258 0.0275
##
## mu = 0.00 2.83 -2.83
##
## sigma = 1.000 1.285 1.257
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9605 0.0213 0.0182
##
## mu = 0.000 3.084 -2.829
##
## sigma = 1.000 1.000 1.371
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9356 0.0324 0.0320
##
## mu = 0.000 2.736 -2.818
##
## sigma = 1.000 1.239 1.153
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9674 0.0166 0.0160
##
## mu = 0.000 -2.820 2.918
##
## sigma = 1.000 1.104 1.338
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9249 0.0369 0.0382
##
## mu = 0.000 -2.987 2.954
##
## sigma = 1.000 1.173 1.176
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9689 0.0162 0.0150
##
## mu = 0.000 2.716 -2.716
##
## sigma = 1.000 1.423 1.205
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9323 0.0332 0.0346
##
## mu = 0.000 -2.932 2.897
##
## sigma = 1.000 1.185 1.125
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9816 0.0096 0.0087
##
## mu = 0.000 2.842 -2.393
##
## sigma = 1.000 1.146 1.810
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9876 0.0062 0.0062
##
## mu = 0.000 -2.165 2.055
##
## sigma = 1.000 1.357 1.649
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9223 0.0389 0.0387
##
## mu = 0.000 2.792 -2.937
##
## sigma = 1.000 1.125 1.185
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9692 0.0151 0.0157
##
## mu = 0.000 2.693 -2.995
##
## sigma = 1.000 1.371 1.425
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9332 0.0334 0.0334
##
## mu = 0.000 -2.929 2.936
##
## sigma = 1.000 1.206 1.129
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9762 0.0124 0.0113
##
## mu = 0.000 -2.915 2.938
##
## sigma = 1.000 1.141 1.326
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9461 0.0253 0.0286
##
## mu = 0.000 3.025 -2.958
##
## sigma = 1.000 1.285 1.133
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9157 0.0449 0.0393
##
## mu = 0.000 -2.897 2.836
##
## sigma = 1.000 1.190 1.181
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9144 0.0452 0.0404
##
## mu = 0.000 -2.881 2.916
##
## sigma = 1.000 1.189 1.128
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9114 0.0452 0.0435
##
## mu = 0.000 2.694 -2.737
##
## sigma = 1.000 1.190 1.229
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9712 0.0134 0.0153
##
## mu = 0.000 2.929 -2.848
##
## sigma = 1.000 1.201 1.409
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9224 0.0391 0.0386
##
## mu = 0.000 2.781 -2.792
##
## sigma = 1.000 1.287 1.230
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9797 0.0098 0.0105
##
## mu = 0.000 -3.129 2.853
##
## sigma = 1.000 1.103 1.404
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9855 0.0078 0.0067
##
## mu = 0.000 -2.361 3.016
##
## sigma = 1.000 1.259 1.176
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9074 0.0449 0.0476
##
## mu = 0.000 2.702 -2.777
##
## sigma = 1.000 1.364 1.213
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9450 0.0297 0.0253
##
## mu = 0.000 2.892 -2.965
##
## sigma = 1.000 1.223 1.040
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9716 0.0140 0.0144
##
## mu = 0.000 2.923 -3.076
##
## sigma = 1.000 1.228 1.046
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9806 0.0087 0.0107
##
## mu = 0.000 -2.815 2.592
##
## sigma = 1.000 1.246 1.708
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9137 0.0419 0.0445
##
## mu = 0.000 2.787 -2.957
##
## sigma = 1.000 1.192 1.169
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9565 0.0226 0.0210
##
## mu = 0.000 2.968 -3.093
##
## sigma = 1.000 1.210 1.249
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9859 0.0069 0.0072
##
## mu = 0.000 2.064 -2.565
##
## sigma = 1.000 1.660 1.024
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9713 0.0145 0.0142
##
## mu = 0.000 -2.766 2.807
##
## sigma = 1.000 1.179 1.032
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9748 0.0120 0.0132
##
## mu = 0.000 -3.271 2.780
##
## sigma = 1.000 1.000 1.435
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9369 0.0293 0.0338
##
## mu = 0.000 -2.921 2.835
##
## sigma = 1.000 1.129 1.306
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9316 0.0356 0.0328
##
## mu = 0.000 -2.819 2.985
##
## sigma = 1.000 1.208 1.305
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9217 0.0413 0.0370
##
## mu = 0.000 -2.791 2.683
##
## sigma = 1.000 1.113 1.328
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9265 0.0328 0.0407
##
## mu = 0.000 2.828 -2.934
##
## sigma = 1.000 1.237 1.079
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9763 0.0112 0.0125
##
## mu = 0.000 -2.855 2.890
##
## sigma = 1.00 1.36 1.29
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9122 0.0475 0.0403
##
## mu = 0.000 2.687 -2.861
##
## sigma = 1.000 1.189 1.060
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9678 0.0162 0.0160
##
## mu = 0.000 -3.036 3.071
##
## sigma = 1.000 1.208 1.235
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9678 0.0175 0.0147
##
## mu = 0.000 2.656 -2.973
##
## sigma = 1.000 1.429 1.227
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9411 0.0302 0.0287
##
## mu = 0.000 2.919 -2.955
##
## sigma = 1.000 1.151 1.135
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9801 0.0096 0.0103
##
## mu = 0.000 3.133 -2.800
##
## sigma = 1.000 1.308 1.309
##
## noiseSD = 1
simres2 = basicsim(c(0, 4), c(1))
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.6207 0.2055 0.1738
##
## mu = 0.000 -3.959 4.111
##
## sigma = 1.000 2.590 2.866
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9319 0.0594 0.0087
##
## mu = 0.000 1.191 -4.996
##
## sigma = 1.000 5.497 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.6679 0.1574 0.1747
##
## mu = 0.000 4.301 -4.192
##
## sigma = 1.000 2.689 2.577
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8613 0.0530 0.0858
##
## mu = 0.000 4.752 -4.335
##
## sigma = 1.000 1.978 2.190
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.5955 0.2104 0.1941
##
## mu = 0.000 -3.865 4.376
##
## sigma = 1.000 2.444 2.598
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7079 0.1637 0.1284
##
## mu = 0.000 -3.962 4.031
##
## sigma = 1.000 2.608 2.414
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.5424 0.2240 0.2336
##
## mu = 0.000 -4.058 4.142
##
## sigma = 1.000 2.741 2.618
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7028 0.1502 0.1470
##
## mu = 0.000 -4.135 4.212
##
## sigma = 1.000 2.778 2.672
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9520 0.0207 0.0272
##
## mu = 0.000 4.883 -5.104
##
## sigma = 1.000 2.119 1.694
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.6278 0.1993 0.1729
##
## mu = 0.000 -4.154 4.380
##
## sigma = 1.000 2.505 2.513
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8082 0.0934 0.0985
##
## mu = 0.000 -4.538 4.220
##
## sigma = 1.000 2.400 2.134
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.4921 0.4423 0.0656
##
## mu = 0.0000 0.7181 -3.5933
##
## sigma = 1.000 4.822 1.114
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.5181 0.2587 0.2233
##
## mu = 0.000 4.370 -4.362
##
## sigma = 1.000 2.932 2.449
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9225 0.0400 0.0375
##
## mu = 0.000 -4.254 4.468
##
## sigma = 1.000 3.259 2.795
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9619 0.0190 0.0191
##
## mu = 0.000 4.931 -4.812
##
## sigma = 1.000 2.354 2.549
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9375 0.0347 0.0277
##
## mu = 0.000 -4.879 3.824
##
## sigma = 1.000 2.150 3.142
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9231 0.0366 0.0402
##
## mu = 0.000 4.052 -4.710
##
## sigma = 1.000 2.611 1.996
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.5765 0.1928 0.2306
##
## mu = 0.000 4.223 -3.940
##
## sigma = 1.000 2.467 2.838
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9274 0.0220 0.0506
##
## mu = 0.000 -4.573 3.128
##
## sigma = 1.000 2.151 3.716
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.5792 0.2222 0.1986
##
## mu = 0.000 -4.159 4.422
##
## sigma = 1.000 2.422 2.316
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8495 0.0745 0.0760
##
## mu = 0.000 -4.152 4.574
##
## sigma = 1.000 2.552 2.246
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9642 0.0187 0.0171
##
## mu = 0.000 -2.946 3.674
##
## sigma = 1.000 1.163 3.483
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.4555 0.2594 0.2851
##
## mu = 0.000 -4.213 4.318
##
## sigma = 1.000 2.486 2.430
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.5668 0.2060 0.2272
##
## mu = 0.000 -4.147 4.239
##
## sigma = 1.000 2.436 2.128
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE TRUE FALSE
##
## pi = 0.7873 0.1828 0.0299
##
## mu = 0.0000 -0.0123 3.2180
##
## sigma = 1.000 4.902 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.6732 0.1597 0.1672
##
## mu = 0.000 -4.033 4.266
##
## sigma = 1.000 2.203 2.565
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9173 0.0356 0.0471
##
## mu = 0.000 -4.742 4.116
##
## sigma = 1.000 1.976 2.448
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.6459 0.1973 0.1568
##
## mu = 0.000 4.319 -4.467
##
## sigma = 1.000 2.425 2.338
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.6592 0.1608 0.1800
##
## mu = 0.000 -4.595 4.306
##
## sigma = 1.000 2.345 2.213
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.6992 0.1251 0.1757
##
## mu = 0.000 4.609 -3.753
##
## sigma = 1.000 2.391 3.179
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: f(z) misfit = 1.5. Rerun with increased df
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD = NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7722 0.0803 0.1476
##
## mu = 0.000 3.956 -4.174
##
## sigma = 1.000 2.330 3.073
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7289 0.1214 0.1496
##
## mu = 0.000 4.222 -4.525
##
## sigma = 1.000 2.368 2.596
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7244 0.1391 0.1365
##
## mu = 0.000 3.957 -4.444
##
## sigma = 1.000 2.334 2.387
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7615 0.1121 0.1263
##
## mu = 0.000 -4.471 3.943
##
## sigma = 1.000 2.291 2.256
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.5116 0.2439 0.2445
##
## mu = 0.000 -4.510 4.342
##
## sigma = 1.000 2.414 2.585
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.5049 0.2284 0.2667
##
## mu = 0.000 4.369 -4.010
##
## sigma = 1.000 2.466 2.699
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7099 0.1465 0.1437
##
## mu = 0.000 -3.867 4.538
##
## sigma = 1.000 2.978 2.491
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.5534 0.2338 0.2128
##
## mu = 0.000 4.348 -4.186
##
## sigma = 1.000 2.240 2.755
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.5548 0.2338 0.2114
##
## mu = 0.000 -3.913 4.298
##
## sigma = 1.000 2.603 2.665
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7694 0.1188 0.1118
##
## mu = 0.000 4.250 -4.373
##
## sigma = 1.000 2.252 2.778
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.4463 0.2737 0.2800
##
## mu = 0.000 4.080 -4.317
##
## sigma = 1.000 2.372 2.134
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8877 0.0546 0.0577
##
## mu = 0.000 -4.522 4.240
##
## sigma = 1.000 2.237 2.728
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9532 0.0167 0.0302
##
## mu = 0.000 -4.571 5.059
##
## sigma = 1.000 2.032 1.772
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9719 0.0161 0.0120
##
## mu = 0.000 -4.200 4.444
##
## sigma = 1.000 4.657 1.911
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9205 0.0427 0.0368
##
## mu = 0.000 3.780 -4.569
##
## sigma = 1.000 1.755 1.798
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9972 0.0014 0.0014
##
## mu = 0.0000 0.4647 0.7202
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.6963 0.1393 0.1644
##
## mu = 0.000 -4.049 4.338
##
## sigma = 1.000 2.174 2.200
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.4852 0.2711 0.2438
##
## mu = 0.000 -4.445 4.496
##
## sigma = 1.000 2.403 2.413
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.4957 0.2464 0.2578
##
## mu = 0.000 -4.432 4.198
##
## sigma = 1.000 2.465 2.414
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9091 0.0208 0.0701
##
## mu = 0.0000 3.6510 -0.6115
##
## sigma = 1.000 1.234 4.831
##
## noiseSD = 1
simres3 = basicsim(c(0, 4), c(1), bsd = c(rep(1, 500), rep(10, 500)))
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7756 0.0667 0.1577
##
## mu = 0.000 -4.211 1.754
##
## sigma = 1.000 2.313 4.311
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9589 0.0344 0.0066
##
## mu = 0.000 1.359 -4.713
##
## sigma = 1.000 5.271 1.496
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8230 0.0901 0.0869
##
## mu = 0.000 -4.024 4.284
##
## sigma = 1.000 2.187 2.904
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9317 0.0406 0.0277
##
## mu = 0.000 -4.144 4.423
##
## sigma = 1.000 2.084 2.024
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7654 0.1935 0.0411
##
## mu = 0.0000 0.7406 -3.2497
##
## sigma = 1.000 4.937 1.216
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE TRUE
##
## pi = 0.8366 0.0353 0.1281
##
## mu = 0.0000 -2.7687 -0.1521
##
## sigma = 1.000 1.000 4.569
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7642 0.1204 0.1155
##
## mu = 0.000 4.127 -3.996
##
## sigma = 1.000 2.637 2.600
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8568 0.0751 0.0681
##
## mu = 0.000 -3.842 3.989
##
## sigma = 1.000 2.684 2.873
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9801 0.0090 0.0109
##
## mu = 0.000 5.483 -4.756
##
## sigma = 1.000 1.475 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8183 0.0935 0.0882
##
## mu = 0.000 -4.187 4.272
##
## sigma = 1.000 2.374 2.523
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9126 0.0424 0.0450
##
## mu = 0.00 -4.79 4.59
##
## sigma = 1.000 2.129 2.119
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7479 0.1239 0.1282
##
## mu = 0.000 -3.953 3.862
##
## sigma = 1.000 2.776 2.582
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7436 0.1283 0.1281
##
## mu = 0.000 4.347 -4.353
##
## sigma = 1.000 2.787 2.638
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9568 0.0098 0.0334
##
## mu = 0.000 3.213 -1.474
##
## sigma = 1.000 1.000 4.777
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9811 0.0110 0.0080
##
## mu = 0.000 4.999 -5.216
##
## sigma = 1.000 2.365 2.597
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9711 0.0218 0.0071
##
## mu = 0.000 -4.695 4.427
##
## sigma = 1.000 2.186 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9661 0.0125 0.0213
##
## mu = 0.000 4.356 -4.537
##
## sigma = 1.000 1.000 1.873
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7680 0.0356 0.1964
##
## mu = 0.0000 -4.0084 0.3638
##
## sigma = 1.000 1.206 4.900
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE TRUE FALSE
##
## pi = 0.9668 0.0263 0.0068
##
## mu = 0.0000 0.1122 2.7411
##
## sigma = 1.000 5.152 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8071 0.0977 0.0952
##
## mu = 0.000 -4.388 4.771
##
## sigma = 1.000 2.793 2.423
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9220 0.0363 0.0417
##
## mu = 0.000 -4.205 4.912
##
## sigma = 1.000 2.422 2.348
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9832 0.0133 0.0035
##
## mu = 0.000 -2.807 6.261
##
## sigma = 1.000 1.304 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7154 0.1450 0.1396
##
## mu = 0.00 -4.04 4.25
##
## sigma = 1.000 2.615 2.499
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7869 0.0969 0.1162
##
## mu = 0.000 -4.166 4.217
##
## sigma = 1.000 2.438 2.271
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9012 0.0787 0.0200
##
## mu = 0.000 1.762 -3.527
##
## sigma = 1.000 5.060 1.337
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8259 0.0285 0.1457
##
## mu = 0.0000 -3.8807 0.7694
##
## sigma = 1.000 1.000 4.545
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9592 0.0236 0.0171
##
## mu = 0.000 4.634 -5.162
##
## sigma = 1.000 2.692 1.481
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: f(z) misfit = 1.7. Rerun with increased df
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD = NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8225 0.0827 0.0948
##
## mu = 0.000 -4.410 4.341
##
## sigma = 1.000 2.608 2.551
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8234 0.0857 0.0909
##
## mu = 0.000 -4.530 4.279
##
## sigma = 1.000 2.354 2.271
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8254 0.0323 0.1424
##
## mu = 0.000 4.365 -1.379
##
## sigma = 1.000 1.439 4.965
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8935 0.0370 0.0696
##
## mu = 0.000 4.104 -4.466
##
## sigma = 1.00 2.61 2.75
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8683 0.0762 0.0555
##
## mu = 0.000 -4.732 4.268
##
## sigma = 1.000 2.639 2.334
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8651 0.0647 0.0701
##
## mu = 0.000 -4.212 3.893
##
## sigma = 1.000 2.590 2.573
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8579 0.1116 0.0305
##
## mu = 0.000 -1.305 4.018
##
## sigma = 1.000 4.367 1.408
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7535 0.1275 0.1190
##
## mu = 0.000 -4.417 4.263
##
## sigma = 1.000 2.527 2.549
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7420 0.1267 0.1313
##
## mu = 0.000 4.126 -3.902
##
## sigma = 1.000 2.596 2.510
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8658 0.0753 0.0589
##
## mu = 0.000 -3.369 4.813
##
## sigma = 1.000 3.173 2.424
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7840 0.1082 0.1079
##
## mu = 0.000 -4.196 4.379
##
## sigma = 1.000 2.512 2.001
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7565 0.1242 0.1193
##
## mu = 0.000 -3.668 4.151
##
## sigma = 1.000 2.930 2.777
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9032 0.0488 0.0480
##
## mu = 0.000 4.346 -4.320
##
## sigma = 1.000 2.223 2.801
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7223 0.1476 0.1300
##
## mu = 0.000 -4.283 4.127
##
## sigma = 1.000 2.307 2.488
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9397 0.0294 0.0309
##
## mu = 0.00 -4.81 4.62
##
## sigma = 1.000 2.454 2.433
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9760 0.0142 0.0098
##
## mu = 0.000 5.253 -4.340
##
## sigma = 1.000 1.745 1.718
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9859 0.0077 0.0064
##
## mu = 0.000 4.593 -4.509
##
## sigma = 1.000 2.070 2.731
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.954 0.024 0.022
##
## mu = 0.000 3.518 -4.497
##
## sigma = 1.000 1.995 1.964
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9972 0.0014 0.0014
##
## mu = 0.0000 0.4647 0.7202
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.8499 0.0697 0.0804
##
## mu = 0.000 -4.045 4.483
##
## sigma = 1.000 2.473 2.161
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: f(z) misfit = 1.6. Rerun with increased df
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD = NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7497 0.1281 0.1223
##
## mu = 0.000 -4.411 4.440
##
## sigma = 1.000 2.256 2.502
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.7265 0.1393 0.1342
##
## mu = 0.000 -3.966 4.097
##
## sigma = 1.000 2.605 2.483
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9701 0.0136 0.0163
##
## mu = 0.000 -2.413 4.441
##
## sigma = 1.000 5.017 2.006
##
## noiseSD = 1
simres4 = basicsim(c(0, 4), c(1), bsd = c(rep(1, 500), rep(10, 500)), minpi0 = 0.9,
seed = 200)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9949 0.0017 0.0034
##
## mu = 0.000 6.049 -3.340
##
## sigma = 1.000 2.870 1.139
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9861 0.0079 0.0060
##
## mu = 0.000 -3.623 3.924
##
## sigma = 1.000 1.381 1.854
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9831 0.0102 0.0066
##
## mu = 0.000 -5.444 5.692
##
## sigma = 1.000 2.764 2.315
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9958 0.0018 0.0023
##
## mu = 0.000 -6.904 2.326
##
## sigma = 1.000 1.218 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9947 0.0031 0.0022
##
## mu = 0.000 -3.470 4.144
##
## sigma = 1.000 1.227 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9851 0.0088 0.0061
##
## mu = 0.000 -6.976 5.662
##
## sigma = 1.000 1.610 1.072
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9900 0.0036 0.0064
##
## mu = 0.000 -6.310 4.703
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9930 0.0029 0.0041
##
## mu = 0.000 5.316 -5.070
##
## sigma = 1.000 1.000 1.142
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9840 0.0109 0.0051
##
## mu = 0.000 4.392 -6.218
##
## sigma = 1.000 2.772 2.526
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9781 0.0089 0.0130
##
## mu = 0.000 -4.965 5.178
##
## sigma = 1.000 2.943 1.922
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9885 0.0048 0.0067
##
## mu = 0.000 -3.797 5.908
##
## sigma = 1.000 1.000 2.229
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9961 0.0020 0.0019
##
## mu = 0.000 -6.604 7.509
##
## sigma = 1.000 2.481 2.715
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9922 0.0046 0.0032
##
## mu = 0.000 4.372 -4.488
##
## sigma = 1.000 1.000 1.008
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9858 0.0092 0.0050
##
## mu = 0.000 5.094 -5.443
##
## sigma = 1.000 3.066 1.482
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9902 0.0050 0.0048
##
## mu = 0.000 3.697 -3.940
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9796 0.0111 0.0093
##
## mu = 0.000 4.144 -5.375
##
## sigma = 1.000 1.772 1.248
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9958 0.0032 0.0010
##
## mu = 0.000 -6.060 7.138
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9880 0.0048 0.0072
##
## mu = 0.000 5.693 -4.072
##
## sigma = 1.000 2.031 2.255
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9940 0.0038 0.0023
##
## mu = 0.000 4.000 3.818
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD = NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9808 0.0067 0.0126
##
## mu = 0.000 -5.838 2.627
##
## sigma = 1.000 1.690 3.831
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9777 0.0039 0.0183
##
## mu = 0.000 -3.710 2.572
##
## sigma = 1.000 1.000 4.878
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9875 0.0048 0.0077
##
## mu = 0.000 -4.251 5.391
##
## sigma = 1.000 1.212 2.243
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9854 0.0042 0.0104
##
## mu = 0.000 4.955 -3.597
##
## sigma = 1.000 3.219 1.266
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9904 0.0041 0.0055
##
## mu = 0.000 -5.437 5.722
##
## sigma = 1.000 2.949 1.427
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9882 0.0034 0.0084
##
## mu = 0.000 -3.492 6.949
##
## sigma = 1.000 1.627 2.829
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9725 0.0089 0.0186
##
## mu = 0.000 5.931 -4.074
##
## sigma = 1.000 2.356 1.715
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9900 0.0023 0.0077
##
## mu = 0.000 -4.905 4.583
##
## sigma = 1.000 1.000 1.246
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9913 0.0060 0.0026
##
## mu = 0.0000 0.6215 -3.4060
##
## sigma = 1.000 6.163 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE TRUE
##
## pi = 0.9970 0.0016 0.0014
##
## mu = 0.0000 0.7701 -0.0227
##
## sigma = 1 1 1
##
## noiseSD = 1
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9959 0.0023 0.0018
##
## mu = 0.000 -5.319 6.127
##
## sigma = 1.000 2.772 1.000
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9820 0.0075 0.0105
##
## mu = 0.000 5.854 -3.265
##
## sigma = 1.000 1.454 1.483
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9873 0.0047 0.0079
##
## mu = 0.000 -7.166 -3.745
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model
## Fitting final model
##
## Fitted Model: J = 3 groups
## ----------------------------
## null? TRUE FALSE FALSE
##
## pi = 0.9969 0.0019 0.0012
##
## mu = 0.000 4.961 -2.635
##
## sigma = 1 1 1
##
## noiseSD = 1
##
##
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted rates numerically 0 occurred
## Error: system is computationally singular: reciprocal condition number =
## 1.78038e-17
Show illustrative example
altcol = "cyan" #colors to use
nullcol = "blue"
nc = 40 #number of bins in histograms
ncz = 100 # number of bins in z score histograms
plot_FDR_hist = function(sim, iter = 1) {
hh.pval = sim$pval[[iter]]
hh.zscore = sim$zscore[[iter]]
hh.hist = hist(hh.pval, freq = FALSE, xlab = "p value", main = "Distribution of p values",
nclass = nc, col = altcol)
hh.q = qvalue(hh.pval)
abline(h = hh.q$pi0, col = nullcol, lwd = 2)
hh.hist$density = rep(hh.q$pi0, length(hh.hist$density))
plot(hh.hist, add = TRUE, col = nullcol, freq = FALSE)
abline(v = 0.1, lwd = 2, col = 2)
text(0.05, 1.2, labels = "A", col = 2, cex = 1.2)
text(0.05, 0.4, labels = "B", col = 2, cex = 1.2)
text(0.6, 3, labels = paste0("FDR = B/(A+B) = ", round(hh.q$pi0 * 0.1 *
length(hh.pval)/sum(hh.pval < 0.1), 2)), cex = 1.2)
}
plot_FDR_hist(simres1, 1)
plot_lfdr_hist = function(sim, iter = 1) {
require(fdrtool)
hh.pval = sim$pval[[iter]]
hh.hist = hist(hh.pval, freq = FALSE, xlab = "p value", main = "Distribution of p values",
nclass = nc, col = altcol)
hh.gren = grenander(ecdf(hh.pval))
abline(h = min(hh.gren$f.knots), col = nullcol, lwd = 2)
lines(hh.gren$x.knots, hh.gren$f.knots, lwd = 2)
abline(v = 0.1, lwd = 2, col = 2)
text(0.1, 0.9, labels = "a", col = 2, cex = 1)
text(0.1, 0.34, labels = "b", col = 2, cex = 1.2)
text(0.6, 3, labels = paste0("lfdr = b/(a+b) = ", round(min(hh.gren$f.knots)/approx(hh.gren$x.knots,
hh.gren$f.knots, 0.1)$y, 2)), cex = 1.2)
}
plot_lfdr_hist(simres1, 1)
# plot a histogram of z scores, highlighting the alternative distribution of
# z scores that is implied by localfdr values lfdr.
nullalthist = function(z, lfdr, ...) {
h = hist(z, freq = FALSE, col = nullcol, nclass = ncz, ...)
avlfdr = unlist(lapply(split(lfdr, cut(z, h$breaks), drop = FALSE), mean))
h$density = (1 - avlfdr) * h$density
plot(h, add = TRUE, col = altcol, freq = FALSE)
}
# this one puts the null on the bottom
altnullhist = function(z, lfdr, ...) {
h = hist(z, freq = FALSE, col = altcol, nclass = ncz, ...)
avlfdr = unlist(lapply(split(lfdr, cut(z, h$breaks), drop = FALSE), mean))
h$density = avlfdr * h$density
plot(h, add = TRUE, col = nullcol, freq = FALSE)
}
plotall_hist = function(sim, iter = 1, histfun = nullalthist) {
hh.zscore = sim$zscore[[iter]]
par(mfcol = c(2, 2))
histfun(hh.zscore, sim$betahat.fdrtool[[iter]]$lfdr, main = "fdrtool")
histfun(hh.zscore, sim$betahat.locfdr[[iter]]$fdr, main = "locfdr")
histfun(hh.zscore, sim$betahat.mixfdr[[iter]]$fdr, main = "mixfdr")
histfun(hh.zscore, sim$betahat.ash.n[[iter]]$lfdr, main = "ash")
par(mfcol = c(1, 1))
}
# pdf('figures/nullalthist.pdf')
plotall_hist(simres1, 1, nullalthist)
# dev.off()
# pdf('figures/altnullhist.pdf')
plotall_hist(simres1, 1, altnullhist)
# dev.off()
# par(mfcol=c(3,3))
plot_ecdf = function(sims) {
for (i in 1:length(sims$beta)) {
plot(ecdf(sims$beta[[i]]), xlim = c(-6, 6), main = paste0("iteration ",
i))
x = seq(-6, 6, length = 1000)
lines(cdf.ash(sims$betahat.ash.n[[i]], x), col = 2, lwd = 2)
lines(cdf.ash(sims$betahat.ash.u[[i]], x), col = 3, lwd = 2)
lines(cdf.ash(sims$betahat.ash.true[[i]], x), col = 4, lwd = 2)
}
}
plot_ecdf(simres1)
plot_ecdf(simres2)
# Plot pi0 from each method
get_pi0.fdrtool = function(f) {
f$param[3]
}
get_pi0.locfdr = function(f) {
f$fp0[1, 3]
}
get_pi0.mixfdr = function(f) {
f$pi[1]
}
plot_pi0 = function(sims) {
pi0 = sims$pi0
pi0_ash.n = unlist(lapply(sims$betahat.ash.n, get_pi0))
pi0_ash.u = unlist(lapply(sims$betahat.ash.u, get_pi0))
pi0_fdrtool = unlist(lapply(sims$betahat.fdrtool, get_pi0.fdrtool))
pi0_locfdr = unlist(lapply(sims$betahat.locfdr, get_pi0.locfdr))
pi0_mixfdr = unlist(lapply(sims$betahat.mixfdr, get_pi0.mixfdr))
pi0_qval = unlist(lapply(sims$betahat.qval, "[[", "pi0"))
res = data.frame(pi0 = pi0, qvalue = pi0_qval, mixfdr = pi0_mixfdr, locfdr = pi0_locfdr,
fdrtool = pi0_fdrtool, ash.nullbiased = pi0_ash.n, ash.uniform = pi0_ash.u)
require(reshape2)
res.melt = melt(res, id.vars = c("pi0"), variable.name = "Method")
p = ggplot(data = res.melt, aes(pi0, value, colour = Method)) + geom_point(shape = 16) +
geom_abline(colour = "black") + xlab("True pi0") + ylab("Estimated pi0")
print(p + scale_y_continuous(limits = c(0, 1)) + scale_x_continuous(limits = c(0,
1)) + coord_equal(ratio = 1))
}
plot_pi1 = function(sims) {
pi0 = sims$pi0
pi0_ash.n = unlist(lapply(sims$betahat.ash.n, get_pi0))
pi0_ash.u = unlist(lapply(sims$betahat.ash.u, get_pi0))
pi0_fdrtool = unlist(lapply(sims$betahat.fdrtool, get_pi0.fdrtool))
pi0_locfdr = unlist(lapply(sims$betahat.locfdr, get_pi0.locfdr))
pi0_mixfdr = unlist(lapply(sims$betahat.mixfdr, get_pi0.mixfdr))
pi0_qval = unlist(lapply(sims$betahat.qval, "[[", "pi0"))
res = data.frame(pi0 = pi0, qvalue = pi0_qval, mixfdr = pi0_mixfdr, locfdr = pi0_locfdr,
fdrtool = pi0_fdrtool, ash.nullbiased = pi0_ash.n, ash.uniform = pi0_ash.u)
require(reshape2)
res.melt = melt(res, id.vars = c("pi0"), variable.name = "Method")
p = ggplot(data = res.melt, aes(1 - pi0, log2((1 - value)/(1 - pi0)), colour = Method)) +
geom_point(shape = 16) + # geom_abline(colour = 'black') +
xlab("True pi1") + ylab("log2(Estimated pi1/True pi1)")
print(p + scale_y_continuous(limits = c(-4, 4)) + scale_x_continuous(limits = c(0,
1)))
}
pdf("figures/estpi0_sim1.pdf")
plot_pi0(simres1)
## Loading required package: reshape2
## Warning: Removed 13 rows containing missing values (geom_point).
dev.off()
## pdf
## 2
pdf("figures/estpi0_sim2.pdf")
plot_pi0(simres2)
dev.off()
## pdf
## 2
Figure to show that estimated betahats are not so different
plot(betahat.ash.u[[1]]$PosteriorMean, betahat.ash.n[[1]]$PosteriorMean)
## Error: object 'betahat.ash.u' not found
abline(a = 0, b = 1, lwd = 2, col = 2)
## Error: plot.new has not been called yet
plot(betahat.ash.u[[1]]$PosteriorSD, betahat.ash.n[[1]]$PosteriorSD)
## Error: object 'betahat.ash.u' not found
abline(a = 0, b = 1, lwd = 2, col = 2)
## Error: plot.new has not been called yet
rmse = function(x, y) {
sqrt(mean((x - y)^2))
}
get_rmse.ash = function(a, b) {
rmse(a$PosteriorMean, b)
}
get_rmse.mixfdr = function(a, b) {
rmse(a$effectSize, b)
}
plot_rmse = function(sims, inczero = FALSE, incbetahat = FALSE) {
err.ash.n = mapply(get_rmse.ash, sims$betahat.ash.n, sims$beta)
err.ash.u = mapply(get_rmse.ash, sims$betahat.ash.u, sims$beta)
err.mixfdr = mapply(get_rmse.mixfdr, sims$betahat.mixfdr, sims$beta)
err.betahat = mapply(rmse, sims$betahat, sims$beta)
err.zero = unlist(lapply(sims$beta, rmse, y = 0))
res = data.frame(mixfdr = err.mixfdr, ash.nullbiased = err.ash.n, ash.uniform = err.ash.u)
if (inczero) {
res = data.frame(res, zero = err.zero)
}
if (incbetahat) {
res = data.frame(res, betahat = err.betahat)
}
require(reshape2)
res.melt = melt(res, id.vars = c("ash.uniform"), variable.name = "Method")
p = ggplot(data = res.melt, aes(ash.uniform, value, colour = Method)) +
geom_point(shape = 16) + geom_abline(colour = "black") + xlab("RMSE (ash.uniform)") +
ylab("RMSE (other method)")
print(p + scale_y_continuous(limits = c(0, max(res))) + scale_x_continuous(limits = c(0,
max(res))) + coord_equal(ratio = 1))
}
pdf("figures/rmse_sim1.pdf")
plot_rmse(simres1)
dev.off()
## pdf
## 2
pdf("figures/rmse_sim2.pdf")
plot_rmse(simres2)
dev.off()
## pdf
## 2
plot_LR = function(sims) {
hist(unlist(lapply(sims$betahat.ash.u, get_loglik)) - unlist(lapply(sims$betahat.ash.n,
get_loglik)), xlab = "loglik difference", main = "loglik differences for nullbiased prior vs mle",
nclass = 10)
}
pdf("figures/logLR.pdf")
plot_LR(simres1)
plot_LR(simres2)
dev.off()
## pdf
## 2
mean_quant = function(x, mult = 1) {
x <- na.omit(x)
sd <- mult * sqrt(var(x))
mean <- mean(x)
data.frame(y = median(x), ymin = quantile(x, 0.25), ymax = quantile(x, 0.75))
}
# ptype indicates what type of plot to do maxlfsr controls maximum x axis
# value maxy controls maximum y axis value
plot_lfsr = function(sims, maxlfsr = 0.1, ptype = c("lfsr", "lfsra", "lfdr"),
maxy = 1) {
ptype = match.arg(ptype)
xlabtype = ifelse(ptype == "lfdr", "lfdr", "lfsr")
res = list()
for (i in 1:length(sims)) {
lfsr.ash.n = unlist(lapply(sims[[i]]$betahat.ash.n, "[[", ptype))
lfsr.ash.u = unlist(lapply(sims[[i]]$betahat.ash.u, "[[", ptype))
if (ptype == "lfdr") {
lfsr.ash.true = unlist(lapply(sims[[i]]$betahat.ash.true, "[[",
"lfdr"))
lfdr.mixfdr = unlist(lapply(sims[[i]]$betahat.mixfdr, "[[", "fdr"))
lfdr.locfdr = unlist(lapply(sims[[i]]$betahat.locfdr, "[[", "fdr"))
} else {
lfsr.ash.true = unlist(lapply(sims[[i]]$betahat.ash.true, "[[",
"lfsr"))
}
subset = lfsr.ash.true < maxlfsr
if (length(subset) > 0) {
res[[i]] = data.frame(Scenario = i, ash.nullbiased = lfsr.ash.n[subset],
ash.uniform = lfsr.ash.u[subset], Bayes = 0.1 * maxlfsr * findInterval(lfsr.ash.true[subset],
seq(0, maxlfsr, length = 11)) - 0.05 * maxlfsr)
if (ptype == "lfdr") {
res[[i]] = data.frame(res[[i]], mixfdr = lfdr.mixfdr[subset])
}
}
}
require(reshape2)
res.melt = melt(res, id.vars = c("Bayes", "Scenario"), variable.name = "Method")
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2",
"#D55E00", "#CC79A7")
labels = c("ash (null-biased)", "ash (uniform)")
breaks = c("ash.nullbiased", "ash.uniform")
if (ptype == "lfdr") {
labels = c("mixfdr", labels)
breaks = c("mixfdr", breaks)
}
p = ggplot(data = res.melt, aes(Bayes, value, colour = Method)) + facet_grid(. ~
Scenario) + # scale_fill_manual(values=cbbPalette) +
# scale_colour_manual(values=cbbPalette) +
geom_point(size = 1, alpha = 0.1) + stat_smooth(se = FALSE, size = 2) +
stat_summary(fun.data = "mean_quant", geom = "ribbon", alpha = 0.25) +
geom_abline(colour = "black") + xlab(paste0(xlabtype, " (Bayes)")) +
ylab(paste0(ptype, " (Method)"))
print(p + scale_y_continuous(limits = c(0, maxy)) + scale_x_continuous(limits = c(0,
maxlfsr)) + scale_colour_manual(values = cbbPalette, breaks = breaks,
labels = labels))
}
# plots estimated lfdr and lfsr against truth. ptype indicates whether to
# use lfsr or lfsra maxx controls maximum x axis value maxy controls maximum
# y axis value
plot_lfsdr = function(sims, maxx = 0.1, ptype = c("lfsr", "lfsra"), maxy = 1) {
ptype = match.arg(ptype)
res = list()
res.lfsr = list()
res.lfdr = list()
for (i in 1:length(sims)) {
lfsr.ash.true = unlist(lapply(sims[[i]]$betahat.ash.true, "[[", "lfsr"))
lfsr.ash.n = unlist(lapply(sims[[i]]$betahat.ash.n, "[[", ptype))
lfsr.ash.u = unlist(lapply(sims[[i]]$betahat.ash.u, "[[", ptype))
lfdr.ash.n = unlist(lapply(sims[[i]]$betahat.ash.n, "[[", "lfdr"))
lfdr.ash.u = unlist(lapply(sims[[i]]$betahat.ash.u, "[[", "lfdr"))
lfdr.ash.true = unlist(lapply(sims[[i]]$betahat.ash.true, "[[", "lfdr"))
lfdr.mixfdr = unlist(lapply(sims[[i]]$betahat.mixfdr, "[[", "fdr"))
lfdr.locfdr = unlist(lapply(sims[[i]]$betahat.locfdr, "[[", "fdr"))
subset = lfsr.ash.true < maxx
res.lfsr[[i]] = data.frame(Scenario = i, Measure = "lfsr", ash.nullbiased = lfsr.ash.n[subset],
ash.uniform = lfsr.ash.u[subset], Bayes = 0.1 * maxx * findInterval(lfsr.ash.true[subset],
seq(0, maxx, length = 11)) - 0.05 * maxx, mixfdr = NA)
subset = lfdr.ash.true < maxx
res.lfdr[[i]] = data.frame(Scenario = i, Measure = "lfdr", ash.nullbiased = lfdr.ash.n[subset],
ash.uniform = lfdr.ash.u[subset], Bayes = 0.1 * maxx * findInterval(lfdr.ash.true[subset],
seq(0, maxx, length = 11)) - 0.05 * maxx, mixfdr = lfdr.mixfdr[subset])
res[[i]] = rbind(res.lfdr[[i]], res.lfsr[[i]])
}
require(reshape2)
res.melt = melt(res, id.vars = c("Bayes", "Scenario", "Measure"), variable.name = "Method")
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2",
"#D55E00", "#CC79A7")
labels = c("mixfdr", "ash (null-biased)", "ash (uniform)")
breaks = c("mixfdr", "ash.nullbiased", "ash.uniform")
p = ggplot(data = res.melt, aes(Bayes, value, colour = Method)) + facet_grid(Measure ~
Scenario) + # scale_fill_manual(values=cbbPalette) +
# scale_colour_manual(values=cbbPalette) +
geom_point(size = 1, alpha = 0.1) + # stat_smooth(se=FALSE,size=2) +
stat_summary(fun.data = "mean_quant", geom = "ribbon", alpha = 0.25) + geom_abline(colour = "red",
size = 1) + xlab("Truth") + ylab("Estimate")
print(p + scale_y_continuous(limits = c(0, maxy)) + scale_x_continuous(limits = c(0,
maxx)) + scale_colour_manual(values = cbbPalette, breaks = breaks, labels = labels))
}
png("figures/lfsdr_sim1sim2_blowup.png", height = 427, width = 720)
plot_lfsdr(list(simres1, simres1a, simres2), 0.1, ptype = "lfsra")
## Warning: Removed 2 rows containing missing values (stat_summary).
## Warning: Removed 4542 rows containing missing values (stat_summary).
## Warning: Removed 50588 rows containing missing values (stat_summary).
## Warning: Removed 14167 rows containing missing values (stat_summary).
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 4542 rows containing missing values (geom_point).
## Warning: Removed 50588 rows containing missing values (geom_point).
## Warning: Removed 14167 rows containing missing values (geom_point).
dev.off()
## pdf
## 2
png("figures/lfdr_sim1sim2_blowup.png", height = 160, width = 540)
plot_lfsr(list(simres1, simres1a, simres2), 0.1, ptype = "lfdr")
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## Warning: Removed 2 rows containing missing values (stat_smooth).
## Warning: Removed 2 rows containing missing values (stat_summary).
## Warning: Removed 2 rows containing missing values (geom_point).
dev.off()
## pdf
## 2
png("figures/lfsra_sim1sim2_blowup.png", height = 160, width = 540)
plot_lfsr(list(simres1, simres1a, simres2), 0.1, ptype = "lfsra")
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## Warning: Removed 1 rows containing missing values (stat_smooth).
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## Warning: Removed 1 rows containing missing values (stat_smooth).
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## Warning: Removed 292 rows containing missing values (stat_smooth).
## Warning: Removed 307 rows containing missing values (stat_smooth).
## Warning: Removed 1 rows containing missing values (stat_summary).
## Warning: Removed 1 rows containing missing values (stat_summary).
## Warning: Removed 599 rows containing missing values (stat_summary).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 599 rows containing missing values (geom_point).
dev.off()
## pdf
## 2
png("figures/lfsra_sim1sim2_blowup.png", height = 160, width = 540)
plot_lfsr(list(simres1, simres1a, simres2), 0.1, ptype = "lfsr")
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## Warning: Removed 9 rows containing missing values (stat_smooth).
## Warning: Removed 7 rows containing missing values (stat_smooth).
## Warning: Removed 16 rows containing missing values (stat_summary).
## Warning: Removed 16 rows containing missing values (geom_point).
dev.off()
## pdf
## 2
plot_pi0(simres3)
## Warning: Removed 1 rows containing missing values (geom_point).
plot_pi1(simres3)
## Warning: NaNs produced
## Warning: Removed 5 rows containing missing values (geom_point).
QUestion: is the null-biased prior maybe a little too conservative? Answer: log likelihoods don't suggest they are
# hh.ashtrue = hh.ashz hh.ashtrue$fitted.g$pi =
# c(2/3,1/15,1/15,1/15,1/15,1/15) hh.ashtrue$fitted.g$mean = c(0,0,0,0,0,0)
# hh.ashtrue$fitted.g$sd = sqrt(c(0,1,0.2,0.4,0.8,3))
# loglik(hh.ashtrue,betahat,sebetahat) loglik(hh.ashz,betahat,sebetahat)